Model Drift

Drift can appear when user behavior, markets, products, fraud patterns, sensors, or other real-world conditions evolve after deployment. Teams monitor input distributions and business outcomes because model accuracy can drop after deployment as production conditions change. This helps practitioners detect degradation early and decide when to retrain, recalibrate, or replace a model used in a production system.

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